Optimization of Cancer Treatment in the Frequency Domain

Area of Science:

• Pharmacometrics and Systems Pharmacology
• Control Engineering Applications in Biology
• Cancer Therapeutics Optimization

Background:

• Conventional pharmacometrics often overlooks detailed exploration of alternative dosing frequencies.
• Quantitative systems pharmacology (QSP) offers potential for novel insights into optimal dosing regimens and drug behaviors.
• Existing methods for QSP-based dosing optimization are limited, especially for complex models like tumor growth.

Purpose of the Study:

• To demonstrate the utility of frequency-domain response analysis (FdRA) for optimizing cancer treatment dosing regimens.
• To apply simulation-based frequency-domain analysis to distinct tumor growth models.
• To investigate the impact of dosing frequency and drug elimination rates on tumor growth and treatment safety.

Main Methods:

• Utilized frequency-domain response analysis (FdRA), a control engineering method, adapted for biological models.
• Analyzed three distinct tumor growth models: cell cycle-specific, metronomic, and acquired resistance.
• Performed simulation-based analyses to assess tumor size response and safety across various dosing frequencies and elimination rates.

Main Results:

• Identified a dosing frequency threshold beyond which tumor size is insensitive to further increases in frequency for all models.
• Demonstrated that certain dosing frequencies, like one dose per 3 days or hourly dosing, can yield similar tumor size reductions.
• Explored the influence of drug elimination rate variations on tumor growth response dynamics.

Conclusions:

• Frequency-domain analysis provides valuable insights for optimizing drug dosing regimens in cancer treatment.
• The findings suggest that excessive increases in dosing frequency may not improve efficacy and can be computationally inefficient.
• This approach can enhance treatment success by identifying optimal dosing strategies based on tumor dynamics.